Many modern devices include a display device that can be used to provide digital content to a user of the device. The digital content can include text, images, sounds, videos, animations, etc. and the user can interact with the digital content. In some instances, the user can access or interact with the digital content and, as the user interacts with or accesses the digital content, the user may create a digital footprint that represents data about the user's digital activities or the user's digital behavior. In some instances, the data can be collected and analyzed for various purposes. For example, data that describes a context surrounding a user's digital behavior (e.g., a context surrounding a user's interaction with digital content or the user's decision to interact with digital content) can be useful for learning the user's digital behavior. As an example, temporal context surrounding the user's digital behavior (e.g., time or day that a user interacts with digital content) can be used to determine the user's digital behavior pattern such as, for example, a time of day that the user typically interacts with digital content.
The expansion of the types of digital content provided to users, along with the expansion of the types of data included in users' digital behavior data sets provides various challenges. For example, data about a user's digital behavior may include robust, complex data sets that include rich, granular, structured, or unstructured data describing the context surrounding the user's digital behavior. Conventional systems and methods may be unable to capture such robust data about the context surrounding the user's digital behavior and may only capture a limited subset of the data. For instance, conventional systems may be limited to capturing only temporal data surrounding the user's digital behavior such as, for example, a time that the user interacts with digital content or a frequency of the user's interactions. Systems and methods using such limited data can inaccurately learn user behavior. Further, conventional systems and methods for capturing and analyzing data describing the context surrounding the user's digital behavior may rely on explicit feedback from the user. However, explicit user feedback data can be sparse (e.g., incomplete) since users may not provide feedback about the context surrounding their digital behavior and systems and methods using such sparse data can inaccurately capture or analyze the context surrounding the user's digital behavior. Moreover, certain conventional systems and methods for capturing and analyzing data describing the context surrounding the user's digital behavior may unethically or illegally capture or collect a user's private information from the user device.
Thus, some existing systems and method for obtaining and analyzing data about a user's digital behavior or the context surrounding the user's digital behavior do not capture robust data about the user's digital behavior or capture private data about the user, which can make it challenging for conventional systems and method to accurately or ethically capture and analyze user digital behavior data or data about the context surrounding the user's digital behavior.